Subjects
Choose questions individually, practise online, or create a worksheet with matching answers. Counts include generated variants, not only authored originals.
No
Aggregate metrics can hide different error patterns and contextual harms.
Verify that the source exists and supports the claim
Citations can be incorrect or fail to support a claim.
0.2
8/40=0.2.
0.1
3/30=0.1; comparison still needs context and uncertainty.
Affected people and operators need a way to challenge errors, fix data and prevent repeated harm. Responsibility must be operational, not merely a slogan.
Credit a concrete correction and accountability pathway.
Use authorized or synthetic data without unnecessary personal details
Minimize exposure and use data appropriately.
Describe who uses the system, who may be affected, and who can correct mistakes. Automation does not remove human responsibility.
Grades 9–12Describe who uses the system, who may be affected, and who can correct mistakes. Automation does not remove human responsibility.
Grades 9–12Understand learning tasks, datasets, honest evaluation, privacy and human responsibility. No paid AI tool is required.
Work through regression, gradients, classification and generalization using paper-based examples.
Compare trees, clustering, PCA and validation without confusing training performance with evidence of generalization.
Build from artificial neurons and backpropagation to attention and embedding geometry.
Practise clear prompting, retrieval-grounded workflows, evidence checking and permission-aware deployment.